A great post from +Alberto Cottica
on how +EdgeRyders
can act as an engine of collective intelligence. Alberto brings together several different important concepts here. He proposes this:
"I think we (Edgeryders) are on our way to becoming the first-ever community to develop its own collective intelligence based on a proper data strategy."
I don't know if I would consider Edgeryders to be the first ever community to develop collective intelligence based on a proper data strategy (that's a large claim!), however there are very few communities who have ventured into this territory. What they're doing is quite innovative and I fully support the direction they're heading in. That is to say, the acknowledgment of the importance of a coherent strategy to deal with the evolution of data and knowledge of a community, and the development of platforms and tools which implement that strategy to support the growth, learning and evolution of that community.
Alberto mentions the importance of structuring the knowledge that's generated by the community around these issues. He describes it as 'ethnographic coding', a technique for qualitative evaluation and research. The result of this ethnographic coding, in this case, being an emerging categorization system of the types of knowledge contributions on the platform that can better enable insights, synthesis and narratives to emerge.
What results from this structured knowledge are new ways to orient, nagivate and explore the knowledge generated by the community. By feeding this back to the community of Edgeryders in different ways, they open the potential to bootstrap their ability to coordinate activities, route opportunities to where its more relevant, identify specific expertise within the community, and collaborate more effectively.
There is much crossover between this effort and perspective that Alberto shares about the evolution of Edgeryders and our work with Metamaps.cc, not only from a semantic or linked data perspective, or the qualitative tagging and categorization of knowledge, but the deeper intention to empower communities through the development of a healthy knowledge ecosystem.
A knowledge ecosystem is defined as "an approach to knowledge management which claims to foster the dynamic evolution of knowledge interactions between entities to improve decision-making and innovation through improved evolutionary networks of collaboration." (wikipedia)
Though this language is a bit dense and can be unfamiliar, the content is key - put in other language we could say that communities are social organisms - they're living and breathing, they change direction, they evolve, they grow and change - and so does our knowledge. As we interact, these atoms of knowledge bond, link, merge with one another, and our collective knowledge evolves. By growing and learning with one another in both physical and virtual collaborative spaces, we can innovate and address challenges together. That's because we have created an environment which is conducive to increasing the capacity of our communities to navigate complexity in its myriad forms. A healthy knowledge ecosystem enables a community's culture to evolve, become more adaptive, and more resilient.
The next paragraph of the definition addresses knowledge ecosystems as an emergence-based approach to knowledge management:
"In contrast to purely directive management efforts that attempt either to manage or direct outcomes, knowledge ecosystems espouse that knowledge strategies should focus more on enabling self-organization in response to changing environments."
I have been a part of quite a few decentralized and distributed communities looking to bring ideas and knowledge to action. A major stumbling block in every single one has been fragmented knowledge, which Alberto mentions at the end of his post. When we spread the conversation across multiple platforms, some of which may not have open data or limited APIs, means that we're less empowered to bootstrap our own learning and evolution. The strength of a distributed community often is co-dependent to an extent on the health of the collaborative knowledge ecosystem which they are a part of, especially when doing a large amount of work in the virtual.
Organizing collaboratively generated knowledge, keeping coherence and momentum within conversations, sense-making complexity, and dealing with nonlinearity are all challenges that we face in this work. Platforms that encourage and engender sensemaking of issues, collaborative evaluation of ideas and synthesis and are key to the health of knowledge ecosystems - hopefully towards enabling what Francis Heylighen of the Global Brain Institute would call a "meta-system transition" - the emergence, through evolution, of a higher level of self-organization.
Alberto and +Matthias Ansorg
(and others!) are working on building both a culture and community which can result in an expanded capacity for 'making sense' of the complexity of issues as well as the social dynamics of collaboration and the evolution of their own culture. In their work is reflected the essential need for tools and platforms that leverage qualitative knowledge categorization, structured data, analysis, and collaboration - things those of us who are actively building Metamaps.cc are also incredibly passionate about. We look forward to seeing what other insights come out of Edgeryders in this direction.